随着人工智能赋能的逐渐深化，产业创新生态系统不断演进。然而，当前对人工智能赋能产业创新生态系统演进内在驱动因素及路径的探讨尚不深入。选取生物医药产业作为案例研究对象，将人工智能应用划分为技术积累期、融合期和产业赋能期，揭示不同赋能阶段产业创新生态系统演进动因与路径。研究发现：①技术积累期由市场竞争引发的生存压力驱动，实现技术单点突破；②技术融合期由政策指引的创新战略驱动，形成端到端的研发模式；③产业赋能期由技术引领的多维合力驱动，形成“AI+自动化实验+专家经验”的模式。研究结论有助于丰富产业创新生态系统理论，为更好地推动人工智能应用、促进产业创新发展提供实践启示。

The integration of artificial intelligence across various sectors, including autonomous vehicles, finance, and biomedicine, has catalyzed significant shifts and advancements in both established and burgeoning industries.Building and developing an innovation ecosystem is an important strategy for countries to promote innovation and development. The application of artificial intelligence can drive technological innovation at the source, attract multiple stakeholders to participate in value creation, integrate innovation resources, and jointly build an innovation ecosystem. With the gradual deepening of artificial intelligence empowerment, it has promoted the dynamic evolution of innovation ecosystems and posed new challenges to the theory of innovation ecosystems. The particularity of artificial intelligence technology has led to a lack of in-depth and systematic research on its specific application process and characteristics in the innovation ecosystem, and the internal driving factors and paths for the evolution of the industrial innovation ecosystem empowered by artificial intelligence are not yet fully understood.This paper adopts a longitudinal single case study method with the biopharmaceutical industry as the case study object. The application of artificial intelligence is divided into a technology accumulation period, an integration period, and an industry empowerment period. From a dynamic perspective, the driving factors and paths of the evolution of the biopharmaceutical industry empowered by artificial intelligence are explored, providing theoretical support and practical guidance for promoting the continuous optimization and upgrading of the industrial innovation ecosystem.The results show that (1) policy guidance, technology promotion, market pull, and organizational change jointly drive the evolution of the innovation ecosystem, and the dominant driving factors that play a role vary in different stages of artificial intelligence technology. (2) Artificial intelligence promotes the evolution of innovation ecosystems through pattern innovation, and innovation patterns achieve a comprehensive transformation from point to end and then to automation. (3) The period of technological accumulation is driven by the survival pressure caused by market competition, achieving a single point breakthrough in technology; the technology integration period is driven by policy-guided innovation strategies, achieving an end-to-end research and development model; and the industrial empowerment period is driven by multidimensional synergy led by technology, achieving a model of "AI+automated experiments+expert experience".The novelties of this paper are as follows: Given the lack of industry analysis at the meso level under technological background in the existing research , this paper conducts case studies on the biopharmaceutical industry to analyze the driving factors and paths of the evolution of artificial intelligence enabled industrial innovation ecosystems, expanding the theoretical research on industrial innovation ecosystems. Moreover, it divides the technology empowerment stage into technology accumulation stage, technology integration stage, and industry empowerment stage, and expands the research on innovation ecosystems from a dynamic perspective. Finally, due to the particularity of artificial intelligence technology, there is a lack of systematic research on the driving factors and paths for the evolution of AI-empowered industrial innovation ecosystems domestically and internationally. This study analyzes the evolution process of the innovation ecosystem in the biopharmaceutical industry, and through model innovation, it facilitates the evolution of the innovation ecosystem.The deep empowerment of artificial intelligence technology and the deep integration of artificial intelligence and industrial innovation ecosystem are the fundamental guarantees for promoting the prosperous and orderly development of industries. At the government level, it is necessary to guide the application of artificial intelligence in different industries, considering the focus of different policies such as supply, demand, and environment. At different stages of technological development, efforts should be made to shift from encouraging technological development to emphasizing regulation, and then to achieving balanced development. At the enterprise level, it is necessary to actively layout artificial intelligence, adjust organizational structure in a timely manner according to the policies and market environment, and achieve a dual wheel drive of "technology+mode". At the same time, in the future, multiple case studies or large-scale empirical studies can also be used to test the research conclusions of this article, further tracking the laws and mechanisms of change, and exploring the empowerment of the biopharmaceutical industry by artificial intelligence from different perspectives.